# Copyright 2024-2025 SpectrixRD
# Licensed under the Apache License, Version 2.0
# See LICENSE file in the project root for full license text

"""
Quantum Backend Integration with BioQL
Handles connection to IBM Torino and quantum execution
"""

import os
from typing import Any, Dict, List, Optional

import numpy as np


class QuantumBackend:
    """
    Interface to BioQL quantum computing platform
    Supports IBM Torino, IonQ, and simulator backends
    """

    def __init__(self, config):
        """
        Initialize quantum backend with BioQL

        Args:
            config: QPHAROSConfig instance
        """
        self.config = config
        self.api_key = config.api_key or os.getenv("BIOQL_API_KEY", "your_api_key_here")
        self.backend_name = config.backend
        self.shots = config.shots
        self.qec_enabled = config.qec_enabled
        self.qec_type = config.qec_type
        self.logical_qubits = config.logical_qubits

        # Import BioQL
        try:
            from bioql import quantum

            self.quantum = quantum
            self.available = True
            print(f"✅ BioQL quantum backend initialized: {self.backend_name}")
        except ImportError:
            print("⚠️  BioQL not installed. Install with: pip install bioql")
            self.available = False
            self.quantum = None

    def execute_circuit(
        self, circuit_description: str, parameters: Optional[Dict[str, Any]] = None
    ) -> Dict[str, Any]:
        """
        Execute quantum circuit via BioQL

        Args:
            circuit_description: Natural language or structured circuit description
            parameters: Additional circuit parameters

        Returns:
            Dictionary with results and metadata
        """
        if not self.available:
            raise RuntimeError("BioQL not available. Please install bioql package.")

        # Build quantum prompt
        prompt = self._build_prompt(circuit_description, parameters)

        # Execute via BioQL
        try:
            result = self.quantum(
                prompt, backend=self.backend_name, shots=self.shots, api_key=self.api_key
            )

            return self._process_result(result)

        except Exception as e:
            print(f"❌ Quantum execution error: {e}")
            raise

    def execute_vqe(
        self,
        hamiltonian_description: str,
        ansatz: str = "hardware_efficient",
        optimizer: str = "cobyla",
    ) -> Dict[str, Any]:
        """
        Execute Variational Quantum Eigensolver

        Args:
            hamiltonian_description: Description of molecular Hamiltonian
            ansatz: Variational ansatz type
            optimizer: Classical optimizer

        Returns:
            Ground state energy and wavefunction
        """
        prompt = f"""
        Execute VQE calculation for:
        {hamiltonian_description}

        Use {ansatz} ansatz with {optimizer} optimizer.
        Target ground state energy with convergence threshold {self.config.convergence_threshold}.
        """

        if self.qec_enabled:
            prompt += f"\nUse {self.qec_type} quantum error correction with {self.logical_qubits} logical qubits."

        return self.execute_circuit(prompt)

    def execute_qaoa(self, problem_description: str, p_layers: int = 3) -> Dict[str, Any]:
        """
        Execute Quantum Approximate Optimization Algorithm

        Args:
            problem_description: Optimization problem description
            p_layers: Number of QAOA layers

        Returns:
            Optimal solution and cost
        """
        prompt = f"""
        Execute QAOA with {p_layers} layers for:
        {problem_description}

        Find minimum cost configuration.
        """

        if self.qec_enabled:
            prompt += f"\nApply {self.qec_type} QEC for fault-tolerant computation."

        return self.execute_circuit(prompt)

    def execute_qgan(
        self,
        training_data_description: str,
        generator_layers: int = 8,
        discriminator_layers: int = 6,
    ) -> Dict[str, Any]:
        """
        Execute Quantum Generative Adversarial Network

        Args:
            training_data_description: Description of training molecules
            generator_layers: Generator circuit depth
            discriminator_layers: Discriminator circuit depth

        Returns:
            Generated molecular candidates
        """
        prompt = f"""
        Train Quantum GAN for molecular generation:
        Training data: {training_data_description}

        Generator: {generator_layers} layers
        Discriminator: {discriminator_layers} layers

        Generate novel molecular structures with similar properties.
        """

        return self.execute_circuit(prompt)

    def molecular_docking(
        self, ligand_smiles: str, protein_pdb: str, binding_site: Optional[str] = None
    ) -> Dict[str, Any]:
        """
        Quantum molecular docking calculation

        Args:
            ligand_smiles: SMILES string of ligand
            protein_pdb: PDB ID or structure
            binding_site: Optional binding site specification

        Returns:
            Binding affinity and interaction details
        """
        prompt = f"""
        Quantum molecular docking:
        Ligand SMILES: {ligand_smiles}
        Protein: {protein_pdb}
        """

        if binding_site:
            prompt += f"\nBinding site: {binding_site}"

        prompt += f"""

        Calculate binding affinity in kcal/mol.
        Identify key molecular interactions (H-bonds, hydrophobic, π-stacking).
        Calculate Ki using thermodynamic formula.
        Predict binding pose with lowest energy.
        """

        if self.qec_enabled:
            prompt += f"\nUse {self.qec_type} QEC with {self.logical_qubits} logical qubits."

        return self.execute_circuit(prompt)

    def admet_prediction(self, molecule_smiles: str) -> Dict[str, Any]:
        """
        ADMET property prediction

        Args:
            molecule_smiles: SMILES string

        Returns:
            ADMET properties (absorption, distribution, metabolism, excretion, toxicity)
        """
        prompt = f"""
        Predict ADMET properties for molecule:
        SMILES: {molecule_smiles}

        Calculate:
        - Absorption: Caco-2 permeability, HIA
        - Distribution: VDss, plasma protein binding
        - Metabolism: CYP450 interactions
        - Excretion: Clearance, half-life
        - Toxicity: hERG, AMES, LD50

        Apply Lipinski's Rule of Five constraints.
        """

        return self.execute_circuit(prompt)

    def _build_prompt(self, description: str, parameters: Optional[Dict[str, Any]] = None) -> str:
        """Build complete prompt for BioQL"""
        prompt = description

        if parameters:
            prompt += "\n\nParameters:\n"
            for key, value in parameters.items():
                prompt += f"- {key}: {value}\n"

        prompt += f"\nBackend: {self.backend_name}"
        prompt += f"\nShots: {self.shots}"

        return prompt

    def _process_result(self, result) -> Dict[str, Any]:
        """Process BioQL result into standardized format"""
        processed = {
            "success": getattr(result, "success", True),
            "backend": getattr(result, "backend", self.backend_name),
            "shots": self.shots,
        }

        # Extract quantum results
        if hasattr(result, "counts"):
            processed["counts"] = result.counts

        if hasattr(result, "energy"):
            processed["energy"] = result.energy

        if hasattr(result, "bio_interpretation"):
            processed["bio_interpretation"] = result.bio_interpretation

        # Extract molecular properties
        bio = getattr(result, "bio_interpretation", {})
        if bio:
            processed.update(
                {
                    "binding_affinity": bio.get("binding_affinity"),
                    "ki": bio.get("ki"),
                    "ic50": bio.get("ic50"),
                    "h_bonds": bio.get("h_bonds"),
                    "hydrophobic_contacts": bio.get("hydrophobic_contacts"),
                    "qed_score": bio.get("qed_score"),
                    "lipinski_pass": bio.get("lipinski_pass"),
                }
            )

        return processed

    def get_backend_info(self) -> Dict[str, Any]:
        """Get information about current backend"""
        return {
            "backend": self.backend_name,
            "shots": self.shots,
            "qec_enabled": self.qec_enabled,
            "qec_type": self.qec_type,
            "logical_qubits": self.logical_qubits,
            "available": self.available,
        }

    def estimate_resources(self, n_qubits: int, circuit_depth: int) -> Dict[str, Any]:
        """
        Estimate quantum resources needed

        Args:
            n_qubits: Number of logical qubits
            circuit_depth: Circuit depth

        Returns:
            Resource estimates
        """
        # Physical qubit overhead for QEC
        qec_overhead = {
            "surface_code": lambda d: (2 * d - 1) ** 2,
            "steane": lambda d: 7,
            "shor": lambda d: 9,
        }

        physical_per_logical = qec_overhead.get(self.qec_type, lambda d: 1)(3)
        total_physical_qubits = n_qubits * physical_per_logical if self.qec_enabled else n_qubits

        # Estimate execution time (rough approximation)
        gate_time_us = 0.1  # 100 ns per gate
        total_gates = n_qubits * circuit_depth * 5  # ~5 gates per layer per qubit
        execution_time_ms = total_gates * gate_time_us / 1000 * self.shots

        return {
            "logical_qubits": n_qubits,
            "physical_qubits": total_physical_qubits,
            "circuit_depth": circuit_depth,
            "total_gates": total_gates,
            "estimated_time_ms": execution_time_ms,
            "qec_overhead": physical_per_logical if self.qec_enabled else 1,
            "shots": self.shots,
        }
